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Research Synthesize

ASecurity

Synthesize research findings from memory into structured reports with evidence grading, contradiction resolution, and actionable recommendations

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  • Added May 27, 2026
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Works with

  • mcp

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A100/100

Scanned May 27, 2026

npx -y skills add ruvnet/ruflo --skill research-synthesize --agent claude-code

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SKILL.md
---
name: research-synthesize
description: Synthesize research findings from memory into structured reports with evidence grading, contradiction resolution, and actionable recommendations
argument-hint: "<topic> [--format report|brief|table]"
allowed-tools: mcp__claude-flow__memory_search mcp__claude-flow__memory_search_unified mcp__claude-flow__memory_list mcp__claude-flow__memory_retrieve mcp__claude-flow__memory_store mcp__claude-flow__agentdb_context-synthesize mcp__claude-flow__agentdb_pattern-search mcp__claude-flow__neural_predict Bash Read Write
---

# Research Synthesize

Synthesize accumulated research findings into actionable reports.

## When to use

After running deep-research (one or multiple times), when you need to pull together findings from memory into a coherent synthesis with recommendations.

## Steps

1. **Gather findings** — search across research namespaces:
   - `mcp__claude-flow__memory_search` namespace `research` for raw findings
   - `mcp__claude-flow__memory_search` namespace `research-sources` for references
   - `mcp__claude-flow__agentdb_pattern-search` for discovered patterns
   - `mcp__claude-flow__agentdb_context-synthesize` for AI-assisted context building
2. **Grade evidence** — for each finding, assess:
   - **High**: Multiple independent sources agree, directly observed, reproducible
   - **Medium**: Single credible source, indirectly supported, plausible
   - **Low**: Anecdotal, single unverified source, speculative
3. **Resolve contradictions** — when findings conflict:
   - Identify the specific claim in tension
   - Compare evidence quality
   - Check recency (newer data may supersede)
   - Note unresolved contradictions explicitly
4. **Predict relevance** — call `mcp__claude-flow__neural_predict` to score which findings are most relevant to the original goal
5. **Structure report**:
   - Executive summary (2-3 sentences answering the original question)
   - Key findings (ranked by evidence quality)
   - Methodology (what sources were checked)
   - Limitations (what wasn't checked, what remains uncertain)
   - Recommendations (concrete next actions)
   - References (source links and memory keys)
6. **Store synthesis** — call `mcp__claude-flow__memory_store` namespace `research-synthesis` with the full report

## Output format

```
# [Research Topic] — Synthesis Report

## Summary
[2-3 sentence answer]

## Key Findings
1. [Finding] — Evidence: High/Medium/Low
2. [Finding] — Evidence: High/Medium/Low

## Contradictions
- [Claim A] vs [Claim B]: [resolution or "unresolved"]

## Recommendations
1. [Action] — because [reasoning]

## Sources
- [key]: [description]
```

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